Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	128,918
2	South Dakota	123,866
3	Rhode Island	113,174
4	Utah	111,460
5	Arizona	108,572
6	Tennessee	107,713
7	Iowa	103,662
8	Wisconsin	103,609
9	Oklahoma	103,039
10	Arkansas	102,703
11	Nebraska	100,754
12	Kansas	98,826
13	Alabama	97,093
14	Indiana	96,027
15	Mississippi	95,342
16	Idaho	93,256
17	Nevada	92,814
18	South Carolina	91,894
19	Wyoming	91,357
20	Illinois	91,210
21	Montana	90,456
22	Louisiana	89,203
23	Texas	87,325
24	California	87,309
25	Georgia	87,304
26	Kentucky	86,631
27	New Mexico	85,266
28	Florida	83,727
29	Delaware	83,655
30	Minnesota	83,333
31	New Jersey	82,487
32	Missouri	82,367
33	Massachusetts	79,643
34	Ohio	79,444
35	New York	77,328
36	North Carolina	77,287
37	Alaska	76,141
38	Connecticut	74,466
39	Colorado	71,262
40	West Virginia	70,279
41	Pennsylvania	69,216
42	Virginia	62,950
43	Michigan	62,550
44	Maryland	60,678
45	District of Columbia	54,336
46	New Hampshire	50,927
47	Washington	43,105
48	Puerto Rico	40,739
49	Oregon	35,202
50	Maine	31,158
51	Vermont	21,029
52	Hawaii	18,721

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Rhode Island	629
2	Connecticut	572
3	South Carolina	438
4	Arizona	430
5	Kentucky	430
6	Texas	428
7	New York	408
8	New Jersey	394
9	Kansas	383
10	Alaska	374
11	Arkansas	353
12	North Carolina	339
13	Virginia	319
14	Oklahoma	317
15	Florida	315
16	Georgia	310
17	Tennessee	303
18	Delaware	296
19	Utah	283
20	California	275
21	Pennsylvania	274
22	Montana	269
23	Iowa	261
24	Idaho	260
25	Alabama	247
26	Massachusetts	242
27	Ohio	239
28	Mississippi	232
29	West Virginia	231
30	New Hampshire	212
31	Louisiana	206
32	New Mexico	196
33	Nevada	188
34	Washington	188
35	Nebraska	183
36	Illinois	180
37	Indiana	178
38	Missouri	168
39	Maryland	166
40	Maine	164
41	Colorado	155
42	Michigan	148
43	District of Columbia	147
44	Wyoming	142
45	Wisconsin	141
46	South Dakota	132
47	Vermont	118
48	Oregon	106
49	Minnesota	105
50	Puerto Rico	68
51	North Dakota	27
52	Hawaii	25

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,498
2	New York	2,305
3	Massachusetts	2,206
4	Mississippi	2,139
5	Rhode Island	2,132
6	Connecticut	2,054
7	South Dakota	2,051
8	Arizona	1,993
9	Louisiana	1,981
10	North Dakota	1,914
11	Alabama	1,812
12	Indiana	1,782
13	Pennsylvania	1,780
14	Illinois	1,725
15	Arkansas	1,714
16	New Mexico	1,651
17	Iowa	1,639
18	Michigan	1,594
19	Tennessee	1,555
20	South Carolina	1,503
21	Nevada	1,488
22	Kansas	1,477
23	Georgia	1,409
24	Texas	1,389
25	District of Columbia	1,367
26	Florida	1,313
27	Delaware	1,278
28	Missouri	1,239
29	Montana	1,235
30	Maryland	1,231
31	West Virginia	1,213
32	Wisconsin	1,152
33	California	1,149
34	Minnesota	1,132
35	Wyoming	1,117
36	Nebraska	1,083
37	Colorado	1,018
38	Ohio	1,014
39	Idaho	1,003
40	Oklahoma	985
41	North Carolina	975
42	Kentucky	971
43	New Hampshire	820
44	Virginia	812
45	Washington	613
46	Puerto Rico	593
47	Utah	550
48	Oregon	488
49	Maine	476
50	Alaska	367
51	Vermont	299
52	Hawaii	296

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Alabama	25
2	Kansas	23
3	Arizona	20
4	Rhode Island	14
5	Wyoming	13
6	Delaware	12
7	Tennessee	12
8	Arkansas	10
9	California	10
10	Connecticut	10
11	Georgia	10
12	Massachusetts	10
13	Mississippi	10
14	Indiana	9
15	New Mexico	9
16	Texas	9
17	Kentucky	8
18	West Virginia	8
19	Florida	7
20	New Jersey	7
21	New York	7
22	Oklahoma	7
23	Pennsylvania	7
24	District of Columbia	6
25	Iowa	6
26	Louisiana	6
27	Nevada	6
28	North Carolina	6
29	Virginia	6
30	Washington	6
31	Maryland	5
32	Ohio	5
33	South Carolina	5
34	Wisconsin	5
35	Idaho	4
36	Nebraska	4
37	Illinois	3
38	Utah	3
39	Colorado	2
40	Michigan	2
41	Missouri	2
42	New Hampshire	2
43	North Dakota	2
44	Oregon	2
45	South Dakota	2
46	Vermont	2
47	Hawaii	1
48	Maine	1
49	Minnesota	1
50	Montana	1
51	Puerto Rico	1
52	Alaska	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	310,840	1	99
Chattahoochee	Georgia	260,567	2	99
Lincoln	Arkansas	238,560	3	99
Bent	Colorado	238,121	4	99
Dewey	South Dakota	235,913	5	99
Davidson	Tennessee	125,101	229	92
Richland	South Carolina	90,928	1288	59
York	South Carolina	84,647	1613	48
Orange	California	80,114	1830	41
Pierce	Washington	40,319	2899	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Buffalo	South Dakota	6,626	3	99
Dickey	North Dakota	6,568	4	99
Emporia city	Virginia	6,547	5	99
Davidson	Tennessee	1,150	1897	39
Richland	South Carolina	1,099	1959	37
Orange	California	1,087	1975	37
York	South Carolina	989	2111	32
Pierce	Washington	554	2674	14

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.